Instructions to use hf-internal-testing/tiny-random-ImageGPTForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ImageGPTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-ImageGPTForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-ImageGPTForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-ImageGPTForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e5d6c69d0b60a5f6009b935bc6c497864cd6021537f88a7d23f6a0fb263de4e
- Size of remote file:
- 5.58 MB
- SHA256:
- 71f678e8a35d77d2c5c478d44abc1ae35b482a8c5f20240b4f278a42db900e71
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